Unsupervised low-light image enhancement by data augmentation and contrastive learning
Author:
Affiliation:
1. Department of Computer Science, University of Sydney - Camperdown and Darlington Campus SciTech Library Australia
2. CAS, Institute of Computing Technology Chinese Academy of Sciences, China
Publisher
Informa UK Limited
Link
https://www.tandfonline.com/doi/pdf/10.1080/13682199.2024.2395751
Reference25 articles.
1. Wei C Wang W Yang W et al. Deep retinex decomposition for low-light enhancement. In: British Machine Vision Conference; 2018.
2. LLNet: A deep autoencoder approach to natural low-light image enhancement
3. Chen C Chen Q Xu J et al. Learning to see in the dark. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2018. pp. 3291–3300.
4. Ultra-high-definition low-light image enhancement: a benchmark and transformer-based method;Wang T;Proc AAAI Conf Artif Intell,2023
5. Agustsson E Timofte R. Ntire 2017 challenge on single image super-resolution: dataset and study. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops; 2017. pp. 126–135.
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